000			01825nab a2200205 4500
005			20260520001756.0
008			260224s2009 xxu
100	1		_aZahedi, G. _942853
100	1		_aFazlali, A.R. _942854
100	1		_aHosseini, S.M. _942855
245	0	0	_aPrediction of asphaltene precipitation in crude oil
260			_coct. 2009
270			_a16/06/2010 ; 16/06/2010
300			_a5 p. ; 218-222
520			_aTranscripción del resumen del autor. Asphaltene are problematic substances for heavy-oil upgrading processes. Deposition of complex and heavy organic compounds, which exist in petroleum crude oil, can cause a lot of problems. In this work an Artificial Neural Networks (ANN) approach for estimation of asphaltene precipitation has been proposed. Among this training the back-propagation learning algorithm with different training methods were used. The most suitable algorithm with appropriate number of neurons in the hidden layer which provides the minimum error is found to be the Levenberg-Marquardt (LM) algorithm. ANN's results showed the best estimation performance for the prediction of the asphaltene precipitation. The required data were collected and after pre-treating was used for training of ANN. The performance of the best obtained network was checked by its generalization ability in predicting 1/3 of the unseen data. Excellent predictions with maximum Mean Square Error (MSE) of 0.2787 were observed. The results show ANN capability to predict the measured data. ANN model performance is also compared with the Flory-Huggins and the modified Flory-Huggins thermo dynamical models. The comparison confirms the superiority of the ANN model.
581			_a3-4
773	0		_tJournal of Petroleum Science & Engineering _g68
942			_cARTICULO
999			_c171113 _d171113